Intelligent Vision Systems for Industry
by Bruce G. Batchelor
Publisher: Springer-Verlag 2002
ISBN/ASIN: 3540199691
ISBN-13: 9783540199694
Number of pages: 473
Description:
The application of intelligent imaging techniques to industrial vision problems is an evolving aspect of current machine vision research. Machine vision is a relatively new technology, more concerned with systems engineering than with computer science, and with much to offer the manufacturing industry in terms of improving efficiency, safety and product quality. Beginning with an introductory chapter on the basic concepts, the authors develop these ideas to describe intelligent imaging techniques for use in a new generation of industrial imaging systems. Sections cover the application of AI languages such as Prolog, the use of multi-media interfaces and multi-processor systems, external device control, and colour recognition. The text concludes with a discussion of several case studies that illustrate how intelligent machine vision techniques can be used in industrial applications.
Download or read it online for free here:
Read online
(online preview)
Similar books
Vision Systemsby David Marshall - Cardiff School of Computer Science
From the table of contents: Image Acquisition: 2D Image Input, 3D imaging; Image processing: Fourier Methods, Smoothing Noise; Edge Detection; Edge Linking; Segmentation; Line Labelling; Relaxation Labelling; Optical Flow; Object Recognition.
(13898 views)
Modern Robotics with OpenCVby Widodo Budiharto - Science Publishing Group
This book is written to provide an introduction to intelligent robotics using OpenCV. It is intended for a first course in robot vision and covers modeling and implementation of intelligent robot. Written for student and hobbyist.
(8616 views)
Notes on Elementary Spectral Graph Theoryby Jean Gallier - arXiv
These are notes on the method of normalized graph cuts and its applications to graph clustering. I provide a thorough treatment of this deeply original method, including complete proofs. The main thrust of this paper is the method of normalized cuts.
(9168 views)
Computer Vision: Models, Learning, and Inferenceby Simon J.D. Prince - Cambridge University Press
This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use data to learn the relationships between the observed image data and the aspects that we wish to estimate.
(22334 views)